Compaction dynamics in ductile granular media

Ductile compaction is common in many natural systems, but the temporal evolution of such systems is rarely studied. We observe surprising oscillations in the weight measured at the bottom of a self-compacting ensemble of ductile grains. The oscillations develop during the first ten hours of the experiment, and usually persist through the length of an experiment (one week). The weight oscillations are connected to the grain--wall contacts, and are directly correlated with the observed strain evolution and the dynamics of grain--wall contacts during the compaction. Here, we present the experimental results and characteristic time constants of the system, and discuss possible reasons for the measured weight oscillations.Comment: 11 pages, 14 figure

Discrete space-time geometry and skeleton conception of particle dynamics

It is shown that properties of a discrete space-time geometry distinguish from properties of the Riemannian space-time geometry. The discrete geometry is a physical geometry, which is described completely by the world function. The discrete geometry is nonaxiomatizable and multivariant. The equivalence relation is intransitive in the discrete geometry. The particles are described by world chains (broken lines with finite length of links), because in the discrete space-time geometry there are no infinitesimal lengths. Motion of particles is stochastic, and statistical description of them leads to the Schr\"{o}dinger equation, if the elementary length of the discrete geometry depends on the quantum constant in a proper way.Comment: 22 pages, 0 figure

Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations

In particle physics the simulation of particle transport through detectors requires an enormous amount of computational resources, utilizing more than 50% of the resources of the CERN Worldwide Large Hadron Collider Grid. This challenge has motivated the investigation of different, faster approaches for replacing the standard Monte Carlo simulations. Deep Learning Generative Adversarial Networks are among the most promising alternatives. Previous studies showed that they achieve the necessary level of accuracy while decreasing the simulation time by orders of magnitudes. In this paper we present a newly developed neural network architecture which reproduces a three-dimensional problem employing 2D convolutional layers and we compare its performance with an earlier architecture consisting of 3D convolutional layers. The performance evaluation relies on direct comparison to Monte Carlo simulations, in terms of different physics quantities usually employed to quantify the detector response. We prove that our new neural network architecture reaches a higher level of accuracy with respect to the 3D convolutional GAN while reducing the necessary computational resources. Calorimeters are among the most expensive detectors in terms of simulation time. Therefore we focus our study on an electromagnetic calorimeter prototype with a regular highly granular geometry, as an example of future calorimeters.Comment: AAAI-MLPS 2021 Spring Symposium at Stanford Universit

Thermo-mechanical homogenization of composite materials

Postprint (published version

Gambini-Pullin Electrodynamics as a scenario for Cherenkov radiation in QED vacuum

We examine the electromagnetic radiation produced by a moving charge in the QED vacuum that behaves as a dispersive medium characterized by a geometrical structure (discreteness/granularity) that emerges from loop quantum gravity. It is shown that the radiation is driven by the refractive vacuum the charged particle travels through reproducing the profile of the Cherenkov effect.Comment: 6 page

Generalizing to new calorimeter geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation

Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive. One subdetector, the calorimeter, dominates the computational time due to the high granularity of its cells and complexity of the interaction. Generative models can provide more rapid sample production, but currently require significant effort to optimize performance for specific detector geometries, often requiring many networks to describe the varying cell sizes and arrangements, which do not generalize to other geometries. We develop a {\it geometry-aware} autoregressive model, which learns how the calorimeter response varies with geometry, and is capable of generating simulated responses to unseen geometries without additional training. The geometry-aware model outperforms a baseline, unaware model by 50\% in metrics such as the Wasserstein distance between generated and true distributions of key quantities which summarize the simulated response. A single geometry-aware model could replace the hundreds of generative models currently designed for calorimeter simulation by physicists analyzing data collected at the Large Hadron Collider. For the study of future detectors, such a foundational model will be a crucial tool, dramatically reducing the large upfront investment usually needed to develop generative calorimeter models